Linked Life Data

15504267

Source:http://linkedlifedata.com/resource/pubmed/id/15504267

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
10
pubmed:dateCreated
2004-10-26
pubmed:abstractText
Conventional disease surveillance mechanisms that rely on passive reporting may be too slow and insensitive to rapidly detect a large-scale infectious disease outbreak; the reporting time from a patient's initial symptoms to specific disease diagnosis takes days to weeks. To meet this need, new surveillance methods are being developed. Referred to as nontraditional or syndromic surveillance, these new systems typically rely on prediagnostic data to rapidly detect infectious disease outbreaks, such as those caused by bioterrorism. Using data from a large health maintenance organization, we discuss the development, implementation, and evaluation of a time-series syndromic surveillance detection algorithm for influenzalike illness in Minnesota.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1080-6040
pubmed:author
pubmed:issnType
Print
pubmed:volume
10
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1806-11
pubmed:dateRevised
2005-11-22
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Syndromic surveillance for influenzalike illness in ambulatory care network.
pubmed:affiliation
University of Minnesota Department of Health, Minneapolis, Minnesota 55414, USA. Benjamin.miller@health.state.mn.us
pubmed:publicationType
Journal Article